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Accurate simulation of rainfall-runoff process is of great importance in hydrology and water resources management. Rainfall–runoff modeling is a non-linear process and highly affected by the inputs to the simulation model. In this study, three kinds of soft computing methods, namely artificial neural networks (ANNs), model tree (MT) and multivariate adaptive regression splines (MARS), have been employed and compared for rainfall-runoff process simulation. Moreover, this study investigates the effect of input size, including number of input variables and number of data time series on runoff simulation by the developed models. Inputs to the simulation models for calibration and validation purposes consist two parts: I1: five variables, including daily rainfall and runoff time series (30 years) with lag times, and I2: twelve variables, including daily rainfall and runoff time series (10 years). To increase the model performances, optimal number and type for input variables are identified. The efficiency of the training and testing performances using the ANNs, MT and MARS models is then evaluated using several evaluation criteria. To implement the methodology, Tajan catchment in the northern part of Iran is selected. Based on the results, it was found that using I1 as input to the developed models results in higher simulation performance. The results also provided evidence that MT (R = 0.897, RMSE = 6.70, RSE = 0.33) with set I2 is capable of reliable model for rainfall-runoff process compared with MARS (R = 0.892, RMSE = 7.47, RSE = 0.83) and ANNs (R = 0.884, RMSE = 7.40, RSE = 0.43) models. Therefore, size (length of data time series) and type of input variables have significant effects on the modeling results.

Compact flicker noise models used in SPICE circuit simulators are derived from the seminal BSIM unified noise model. In this paper, we show that use of this model can give anomalous bias dependence of input referred noise. In addition, we find that the state-of-the art flicker noise models are not adequate to capture the drain bias dependence of flicker noise in short channel devices. In this paper, we address both the issues with a new compact model.

Purpose Parents are important figures in properly managing youth sport concussions. Although media attention has predominantly centered on concussions in contact/collision sports, evidence suggests that the concussion rate in soccer is comparable to those found in contact/collision sports. Given the high rate of concussions in youth soccer, this study aimed to examine parents of youth soccer athletes' attitudes and perceptions about concussions and associated factors. Methods A cross-sectional study was conducted by surveying parents of youth soccer athletes from the five largest organized youth soccer programs across the U.S. The researchers developed a questionnaire after an extensive literature review and by modifying previously used instruments. Results Overall, 419 parents completed the survey. The vast majority (85%) agreed that a concussion is a serious injury, but only 27.9% believed that their child could suffer a concussion during the next season. Parents were most concerned about permanent brain damage when their child suffers a concussion. The vast majority (4.37 ± .89) perceived concussion reporting as an important injury prevention strategy. Greater appreciation and perceived risk about concussions was found particularly among parents who received concussion education and those who had witnessed or heard about a concussive incidence(s). Conclusions Findings suggest that youth soccer parents have high appreciation and perceived risk about concussions. However, the need for more targeted education was noted, as improvements to better manage and reduce concussions can be made. Future research should continue examining youth sport parents' belief and understanding about concussions as well as factors affecting them.

In this study, coating equipment for the effective underwater repair of submerged structures was developed. The tensile bond characteristics of selected epoxy resin coatings were investigated by coating the surface of a specimen using each of the four types of equipment. Using the experimental results, the tensile bond strength and the coating thickness were analyzed according to the type of equipment, coating, and curing time. The results show that the type of coating equipment used had the greatest effect on the measured bond strength and coating thickness of the selected coatings. However, the effect of coating type and curing time on the bond strength and the thickness was observed to be insignificant. Compared with the developed equipment, the surface treatment of the coating was observed to be more effective when using the pre-existing equipment, and thus the bond performance of the coating was improved compared to using the pre-existing equipment. Based on the experimental results, improvements and needs involving the equipment for further research were discussed.

Eimeria maximais a common cause of coccidiosis in chickens, a disease which has a huge economic impact on poultry production. Knowledge of immunity toE. maximaand the specific mechanisms that contribute to differing levels of resistance observed between chicken breeds and between congenic lines derived from a single breed of chickens is required. This study aimed to define differences in the kinetics of the immune response of two inbred lines of White Leghorn chickens that exhibit differential resistance (line C.B12) or susceptibility (line 15I) to infection byE. maxima. Line C.B12 and 15I chickens were infected withE. maximaand transcriptome analysis of infected jejunal tissue was carried out at 2, 4, 6 and 8 days post-infection (dpi). RNA-Seq analysis revealed differences in the rapidity and magnitude of cytokine transcription responses post-infection between the two lines. In particular, IFN-γ and IL-10 transcripts in the jejunum accumulated earlier in line C.B12 (at 4 dpi) compared to line 15I (at 6 dpi). Line C.B12 chickens exhibited increases ofIFNGandIL10mRNA in the jejunum at 4 dpi, whereas in line 15I transcription was delayed but increased to a greater extent. RT-qPCR and ELISAs confirmed the results of the transcriptomic study. Higher serum IL-10 correlated strongly with higherE. maximareplication in line 15I compared to line C.B12 chickens. Overall, the findings suggest early induction of the IFN-γ and IL-10 responses, as well as immune-related genes at 4 dpi identified by RNA-Seq, may be key to resistance toE. maxima.

The precise forecasting of daily solar radiation (DSR) is receiving prominent attention among thriving solar energy studies. In this study, three standalone models, including gene expression programing (GEP), multivariate adaptive regression splines (MARS), and self-adaptive MARS (SaMARS), were evaluated to forecast DSR. A SaMARS model was classified as MARS model when using the crow search algorithm (CSA). In addition, to overcome the limitations of the standalone models, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was employed to enhance the accuracy of DSR forecasting. Therefore, three hybrid models including CEEMDAN-GEP, CEEMDAN-MARS, and CEEMDAN-SaMARS were proposed to forecast DSR in Busan and Incheon stations in South Korea. The performance of proposed models were evaluated and affirmed that the accuracy of the CEEMDAN-SaMARS model (NSE = 0.878–0.883) outperformed CEEMDAN-MARS (NSE = 0.819–0.818), CEEMDAN-GEP (NSE = 0.873–0.789), SaMARS (NSE = 0.846–0.769), MARS (NSE = 0.819–0.758), and GEP (NSE = 0.814–0.755) models at both stations. Therefore, it can be concluded that the optimized CEEMDAN-SaMARS model significantly enhanced the accuracy of DSR forecasting compared to that of standalone models.

Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem. The present research investigates the capacity of several types of AI models for modeling four key water quality variables namely electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solid (TDS) and Sulfate (SO4) using dataset obtained from 90 wells in Tabriz Plain, Iran; assessed by k-fold testing. Two different modeling scenarios were established to make simulations using other quality parameters and the geographical information. The obtained results confirmed the capabilities of the AI models for modeling the well groundwater quality variables. Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. The introduced computer aid methodology provided a reliable technology for groundwater monitoring and assessment.

Biomass derived carbon via hydrothermal carbonization are critically important for the catalysis research field requiring high activity with low cost. However, most of them present low activity due to low surface areas with large particle size originating from unnecessary compounds in raw status. Here, we report a porous carbon decorated with Fe3C/Fe3O4 nanoparticles via pretreatment of pig blood (PB) by employing solvent pretreatment and pyrolyzing in regulated environment using a Fe-porphyrin-type macrocycle as catalyst precursors. Importantly, the addition of toluene to the raw form of PB as pretreatment plays a significant role in not only producing the nanoparticles with porous carbon materials but also removing impurities that deteriorate the active sites of molecular nitrogen carbon (MNC) type catalysts during high-temperature activation. Furthermore, the temperature for hydrothermal treatment and pyrolysis influences oxygen reduction reaction (ORR) performances. The highest-performing PB-derived catalyst delivered its kinetic current and the degree of degradation (after 10,000 potential cycles) were 1.57 mA/cm2 (at 0.9 V) and 19 mV (half-wave potential), and those of Pt/C were 1.26 mA/cm2 and 43 mV, respectively. The catalysts were prepared by applying pretreatment to the PB and characterized systematically to investigate how such pretreatment influences the physical properties and ORR performances.

Owing to the reduction of surface-water resources and frequent droughts, the exploitation of groundwater resources has faced critical challenges. For optimal management of these valuable resources, careful studies of groundwater potential status are essential. The main goal of this study was to determine the optimal network structure of a Bayesian network (BayesNet) machine-learning model using three metaheuristic optimization algorithms—a genetic algorithm (GA), a simulated annealing (SA) algorithm, and a Tabu search (TS) algorithm—to prepare groundwater-potential maps. The methodology was applied to the town of Baghmalek in the Khuzestan province of Iran. For modeling, the location of 187 springs in the study area and 13 parameters (altitude, slope angle, slope aspect, plan curvature, profile curvature, topography wetness index (TWI), distance to river, distance to fault, drainage density, rainfall, land use/cover, lithology, and soil) affecting the potential of groundwater were provided. In addition, the statistical method of certainty factor (CF) was utilized to determine the input weight of the hybrid models. The results of the OneR technique showed that the parameters of altitude, lithology, and drainage density were more important for the potential of groundwater compared to the other parameters. The results of groundwater-potential mapping (GPM) employing the receiver operating characteristic (ROC) area under the curve (AUC) showed an estimation accuracy of 0.830, 0.818, 0.810, and 0.792, for the BayesNet-GA, BayesNet-SA, BayesNet-TS, and BayesNet models, respectively. The BayesNet-GA model improved the GPM estimation accuracy of the BayesNet-SA (4.6% and 7.5%) and BayesNet-TS (21.8% and 17.5%) models with respect to the root mean square error (RMSE) and mean absolute error (MAE), respectively. Based on metric indices, the GA provides a higher capability than the SA and TS algorithms for optimizing the BayesNet model in determining the GPM.

Chlorophyll-a is one of the main indicators for water quality (WQ) analysis in environmental monitoring of aquatic ecosystems. WQ degradation is mostly a result of the increase of the concentration of chlorophyll-a in a waterbody, however, proper estimation of daily chlorophyll-a concentration is a complex problem. In this study, the classic extreme learning machine (ELM), group method of data handling (GMDH), random forest (RF), classification and regression tree (CART), and a novel integrated Bat-ELM model (with the bat optimization algorithm) were developed and applied to predict daily chlorophyll-a (Chl-a) concentration in river and lake ecosystems. Input parameters including turbidity (TU), pH, specific conductance (SC), water temperature (TE), and periodicity were applied as the influential elements for estimating daily Chl-a concentration for two different USGS stations. General results based on RMSE values indicated that the Bat-ELM as the most robust model improved the performance of standard ELM, GMDH, RF, and CART models during the testing procedure by 20.7%, 23.9%, 18.3%, and 27.4% in USGS no. 05543010 and 13.8%, 16.8%, 17.5%, and 52.0% in USGS no. 09014050 in terms of the 7th input-combination, respectively. Moreover, the results revealed that periodicity is the most effective input parameter that considered as the last scenario (input combination) on daily chlorophyll-a (Chl-a) concentration.

Modelling reference evapotranspiration (ET0) is a key issue in hydrology, water resources management and irrigation scheduling. This article aimed at evaluating gene expression programming (GEP) and support vector machine (SVM) techniques for modelling ET0 in humid regions of South Korea. The GEP and SVM results were also compared with the empirical ET0 models with the same input parameters. Daily meteorological data from eight weather stations comprising a period of 10 years were utilized to establish the models. This study evaluated two different data management scenarios of simulations. At the first scenario, the heuristic and empirical models were developed and tested at each location, separately, which confirmed the superiority of heuristic models to empirical models. At the second scenario, two cross-station approaches were developed: modelling ET0 using data from ancillary stations and developing generalized heuristic models for the studied stations. The obtained results revealed that both the GEP and SVM techniques could simulate ET0 in both the scenarios, successfully. Meanwhile, GEP outperformed SVM in cross-station scenario.

This research conducts one-to-one survey in respect of the users' state for the Suseong Pond, Sincheon and Palgeocheon, which have different characteristics of Daegu City, South Korea. From the research results, it is found that the Suseong Pond has many facilities to enjoy such as cafe, restaurant, amusement park, small boat etc. The facilities are highly used by young adult group (20 and 30s) with well-established trails, sports facilities, etc. The Sincheon is utilized for walking and exercising by the senior group (60s) with festival, educational experience and theme, amusement facilities, etc. The Palgeocheon, which is a waterside space of downtown area, is utilized for walking and exercising by the residents group (40 and 50s), who mostly live within 1 km from the stream, with amusement facilities, convenience facilities etc. Based on this research, we propose that the characteristics of waterside and its surrounding area should be investigated and considered for the development of waterfront space to reflect users' characteristics and requirements. The development of waterfront space reflecting the characteristics of users, stream, lake and facilities is expected to attract more users and enhance their satisfaction significantly.

This study develops a hybrid model, EEMD-FANN, coupling feed Forward Artificial Neural Network (FANN) and Ensemble Empirical Mode Decomposition (EEMD) for improving the accuracy of daily river stage forecasting. An original river stage data is broken down into a residue and Intrinsic Mode Functions (IMFs) using the EEMD and different FANNs are developed as forecasting models for the decomposed IMFs and residue, respectively. The final forecasted time series is produced by the ensemble aggregation of the forecasted IMFs and residue. The efficiency of EEMD-FANN model is assessed based on the comparison with that of single Adaptive Neuro-Fuzzy Inference System (ANFIS) and FANN to demonstrate the applicability of the hybrid approach in daily river stage forecasting. As a result, it is found that the EEMD-FANN model utilizing time series decomposition by the EEMD and ensemble aggregation produces better performance than the single ANFIS and FANN models using original river stage time series as inputs. The results of this study also signify that the approach coupling the EEMD and FANN can significantly enhance the forecasting ability of the single FANN model and can be utilized as an effective modeling methodology to forecast river stage precisely.

Interleukin (IL)-17D is a proinfiammatory cytokine with currently largely unknown biological functions. Here we provide the description of the sequence, bioactivity, and mRNA expression profile of duck IL-17D homologue. A full-length duck IL-17D (duIL-17D) cDNA with a 624-bp coding region was identified from the large intestine. dull-17D shares approximately 94.7% identity with its chicken counterpart, which is also identified in this work. duIL-17D exhibits 62.6-68.4% and 52.1-53.1% identity with mammalian and piscine homologues. Recombinant duIL-17D promoted the expression of proinfiammatory cytokines such as IL-6, IL-8, and IL-1 beta in duck embryo fibroblast cells. Very low levels of dull-17D transcript were observed in healthy lymphoid tissues, including bursa, thymus, and spleen, while duIL-17D expression was relatively high in the heart. The dull-17D expression profiles were examined in mitogen-stimulated splenic lymphocytes, as well as tissues affected by Riemerella anatipestifer infection. The levels of duIL-17D were mostly upregulated in mitogen-activated splenic lymphocytes but downregulated in the liver and spleen of R. anatipestifer-infected ducks. These results provide new insights into the roles of IL-17D in host protective immune responses to Riemerella infection, which can therefore lead to further studies of its biological functions in different disease models of ducks and other avian species. Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2015R1D1A1A02059953] This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A1A02059953). Public domain – authored by a U.S. government employee

This study develops and applies three hybrid models, including wavelet packet-artificial neural network (WPANN), wavelet packet-adaptive neuro-fuzzy inference system (WPANFIS) and wavelet packet-support vector machine (WPSVM), combining wavelet packet decomposition (WPD) and machine learning models, ANN, ANFIS and SVM models, for forecasting daily river stage and evaluates their performance. The WPANN, WPANFIS and WPSVM models using inputs decomposed by the WPD are found to produce higher efficiency based on statistical performance criteria than the ANN, ANFIS and SVM models using original inputs. Performance evaluation for various mother wavelets indicates that the model performance is dependent on mother wavelets and the WPD using Symmlet-10 and Coiflet-18 is more effective to enhance the efficiency of the conventional machine learning models than other mother wavelets. It is found that the WPANFIS model outperforms the WPANN and WPSVM models, and the WPANFIS14-coif18 model produces the best performance among all other models in terms of model efficiency. Therefore, the WPD can significantly enhance the accuracy of the conventional machine learning models, and the conjunction of the WPD and machine learning models can be an effective tool for forecasting daily river stage accurately .

The spine is the most common site of skeletal involvement in tuberculosis. The radiologic features are reportedly characterized by destruction of the vertebral body, subligamentous extension or subchondral penetration, frequent paravertebral abscess formation and late involvement of the disk space. We experienced a case of a 25-year-old male who was a human immunodeficiency virus carrier without antiretroviral therapy. Incidental findings on abdominal computed tomography included multiple well-demarcated and ovoid osteolytic lesions with hyperdense rims disseminated in the thoracic, lumbar, and sacrum vertebrae, as well as in both ilii. On the lumbar spine magnetic resonance imaging, multiple small round lesions of isointense signal intensity with peripheral hyperintense rims were found on both T1- and T2-weighted imaging. The lesions had peripheral rim enhancement on gadolinium-enhanced T1-weighted imaging. Based on our experience, this rare image finding is one of the manifestations of disseminated tuberculosis.

The objective of this study is to develop hybrid models by combining data-driven models, including support vector machines (SVM) and generalized regression neural networks (GRNN), and wavelet decomposition for aggregation and disaggregation of rainfall. The wavelet-based support vector machines (WSVM) and wavelet-based generalized regression neural networks (WGRNN) models are obtained using mother wavelets, including db8, db10, sym8, sym10, coif6, and coif12. The developed models are evaluated in the Bocheong-stream catchment, an International Hydrological Program representative catchment, Republic of Korea. WSVM and WGRNN models with mother wavelet db10 yield the best performance as compared with other mother wavelets for estimating areal and disaggregated rainfalls, respectively. Among 12 rainfall stations, SVM, GRNN, WSVM (db10 and sym10), and WGRNN (db10 and sym10) models provide the best accuracies for estimating the disaggregated rainfalls at Samga (No. 7), and the worst accuracies for estimating the disaggregated rainfalls at Yiweon (No. 11) stations, respectively. Results obtained from this study indicate that the combination of data-driven models and wavelet decomposition can be a useful tool for estimating areal and disaggregated rainfalls satisfactorily, and can yield better efficiency than data-driven models.

We synthesized a heterotetrapod composed of a chalcopyrite(ch)-CuInS2 core and wurtzite(w)-CdS arms and elucidated its optical properties and light-stimulated carrier dynamics using fs-laser flash photolysis. The CuInS2/CdS heterotetrapod possessed quasi-type II band alignment, which caused much longer-lived charge separation than that in the isolated CuInS2 nanocrystal.

The objective of this study is to develop the hybrid model for estimating solar irradiance and investigate its accuracy for optimal photovoltaic system. The hybrid model is wavelet-based support vector machines (WSVMs) and wavelet-based adaptive-neuro fuzzy inference system (WANFIS). Wavelet decomposition is employed to decompose the global solar irradiance time series components into approximation and detail components. These decomposed time series are then used as input to support vector machines (SVMs) modules in the WSVMs model and adaptive-neuro fuzzy inference system (ANFIS) modules in the WANFIS. Based on statistical indices, results indicate that WSVMs and WANFIS can successfully be used for the estimation of global solar irradiance at Big bend, Carbondale, Champaign, and Springfield stations in Illinois.

Intracranial lesions may show contrast enhancement through various mechanisms that are closely associated with the disease process. The preferred magnetic resonance sequence in contrast imaging is T1-weighted imaging (T1WI) at most institutions. However, lesion enhancement is occasionally inconspicuous on T1WI. Although fluid-attenuated inversion recovery (FLAIR) sequences are commonly considered as T2-weighted imaging with dark cerebrospinal fluid, they also show mild T1-weighted contrast, which is responsible for the contrast enhancement. For several years, FLAIR imaging has been successfully incorporated as a routine sequence at our institution for contrast-enhanced (CE) brain imaging in detecting various intracranial diseases. In this pictorial essay, we describe and illustrate the diagnostic importance of CE-FLAIR imaging in various intracranial pathologic conditions.

Nutrients are absorbed in the small intestine through a variety of transporter proteins, which have not been as well characterized in turkeys as in chickens. The objective of this study was to profile the mRNA expression of amino acid and monosaccharide transporters in the small intestine of male and female turkeys. Jejunum was collected during embryonic development (embryonic d 21 and 24, and d of hatch (DOH)) and duodenum, jejunum, and ileum were collected in a separate experiment during posthatch development (DOH, d 7, 14, 21, and 28). Real-time PCR was used to determine expression of aminopeptidase N (APN), one peptide (PepT1), 6 amino acid (ASCT1, b(o,+)AT, CAT1, EAAT3, LAT1, y(+)LAT2) and 3 monosaccharide (GLUT2, GLUT5, SGLT1) transporters. Data were analyzed by ANOVA using JMP Pro 11.0. APN, b(o,+)AT, PepT1, y(+)LAT2, GLUT5, and SGLT1 showed increased expression from embryonic d 21 and 24 to DOH. During posthatch, all genes except GLUT2 and SGLT1 were expressed greater in females than males. GLUT2 was expressed the same in males as females and SGLT1 was expressed greater in males than females. All basolateral membrane transporters were expressed greater during early development then decreased with age, while the brush border membrane transporters EAAT3, GLUT5, and SGLT1 showed increased expression later in development. Because turkeys showed high-level expression of the anionic amino acid transporter EAAT3, a direct comparison of tissue-specific expression of EAAT3 between chicken and turkey was conducted. The anionic amino acid transporter EAAT3 showed 6-fold greater expression in the ileum of turkeys at d 14 compared to chickens. This new knowledge can be used not only to better formulate turkey diets to accommodate increased glutamate transport, but also to optimize nutrition for both sexes.

This study develops and applies hybrid models combining wavelet packet decomposition and data-driven models for forecasting river stage and investigating their accuracy. The hybrid models are wavelet packet-based artificial neural network (WPANN) and wavelet packet-based adaptive neuro-fuzzy inference system (WPANFIS). Wavelet packet decomposition splits an input time series into approximation and detail components, and the decomposed time series are used as inputs to artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WPANN and WPANFIS models, respectively. The forecasting performances of WPANN and WPANFIS models are compared with those of ANN and ANFIS models. Based on performance evaluation indexes and graphical comparison, the WPANN and WPANFIS models produce better performance than ANN and ANFIS models in terms of model efficiency, and WPANFIS-coif18 model is also found to yield the best performance among all other models. Therefore, wavelet packet decomposition improves the accuracy of ANN and ANFIS models for forecasting river stage. The results indicate that river stage forecasting models combining wavelet packet decomposition and data-driven models can be used as an effective tool for forecasting river stage accuracy.

The objective of this study is to develop the hybrid models by combining neural computation, including support vector machines (SVM) and generalized regression neural networks (GRNN), and wavelet technique for rainfall modeling. The wavelet-based support vector machines (WSVM) and wavelet-based generalized regression neural networks (WGRNN) models are obtained using mother wavelets, including db8, db10, sym8, sym10, coif6, and coif12. The developed models are evaluated in the Bocheong-stream catchment, an International Hydrological Program (IHP) representative catchment, Republic of Korea. Results obtained from this study indicate that the combination of neural computing and wavelet technique can be a useful tool for modeling of rainfall satisfactorily and can yield better efficiency than neural computing.

Streamflow forecasting is a vital task for hydrology and water resources engineering, and the different artificial intelligence (AI) approaches have been employed for this purposes until now. Additionally, the forecasting accuracy and uncertainty estimation are the meaningful assignments that need to be recognized. The addressed research investigates the potential of novel ensemble approach, Bayesian model averaging (BMA), in streamflow forecasting using daily time series data from two stations (i.e., Hongcheon and Jucheon), South Korea. Six categories (i.e., M1&ndash, M6) of input combination using different antecedent times were employed for streamflow forecasting. The outcomes of BMA model were compared with those of multivariate adaptive regression spline (MARS), M5 model tree (M5Tree), and Kernel extreme learning machines (KELM) models considering four assessment indexes, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and mean absolute error (MAE). The results revealed the superior accuracy of BMA model over three machine learning models in daily streamflow forecasting. Considering RMSE values among the best models during testing phase, the best BMA model (i.e., BMA2) enhanced the forecasting accuracy of MARS1, M5Tree4, and KELM3 models by 5.2%, 5.8%, and 3.4% in Hongcheon station. Additionally, the best BMA model (i.e., BMA1) improved the forecasting accuracy of MARS1, M5Tree1, and KELM1 models by 6.7%, 9.5%, and 3.7% in Jucheon station. In addition, the best BMA models in both stations allowed the uncertainty estimation, and produced higher uncertainty of peak flows compared to that of low flows. As one of the most robust and effective tools, therefore, the BMA model can be successfully employed for streamflow forecasting with different antecedent times.

Accurate estimation of dew point temperature (Tdew) has a crucial role in sustainable water resource management. This study investigates kernel extreme learning machine (KELM), boosted regression tree (BRT), radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), and multivariate adaptive regression spline (MARS) models for daily dew point temperature estimation at Durham and UC Riverside stations in the United States. Daily time scale measured hydrometeorological data, including wind speed (WS), maximum air temperature (TMAX), minimum air temperature (TMIN), maximum relative humidity (RHMAX), minimum relative humidity (RHMIN), vapor pressure (VP), soil temperature (ST), solar radiation (SR), and dew point temperature (Tdew) were utilized to investigate the applied predictive models. Results of the KELM model were compared with other models using eight different input combinations with respect to root mean square error (RMSE), coefficient of determination (R2), and Nash&ndash, Sutcliffe efficiency (NSE) statistical indices. Results showed that the KELM models, using three input parameters, VP, TMAX, and RHMIN, with RMSE = 0.419 &deg, C, NSE = 0.995, and R2 = 0.995 at Durham station, and seven input parameters, VP, ST, RHMAX, TMIN, RHMIN, TMAX, and WS, with RMSE = 0.485 &deg, C, NSE = 0.994, and R2 = 0.994 at UC Riverside station, exhibited better performance in the modeling of daily Tdew. Finally, it was concluded from a comparison of the results that out of the five models applied, the KELM model was found to be the most robust by improving the performance of BRT, RBFNN, MLPNN, and MARS models in the testing phase at both stations.

The biochemical oxygen demand (BOD), one of widely utilized variables for water quality assessment, is metric for the ecological division in rivers. Since the traditional approach to predict BOD is time-consuming and inaccurate due to inconstancies in microbial multiplicity, alternative methods have been recommended for more accurate prediction of BOD. This study investigated the capability of a novel deep learning-based model, Deep Echo State Network (Deep ESN), for predicting BOD, based on various water quality variables, at Gongreung and Gyeongan stations, South Korea. The model was compared with the Extreme Learning Machine (ELM) and two ensemble tree models comprising the Gradient Boosting Regression Tree (GBRT) and Random Forests (RF). Diverse water quality variables (i.e., BOD, potential of Hydrogen (pH), electrical conductivity (EC), dissolved oxygen (DO), water temperature (WT), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (T-N), and total phosphorus (T-P)) were utilized for developing the Deep ESN, ELM, GBRT, and RF with five input combinations (i.e., Categories 1–5). These models were evaluated by root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), coefficient of determination (R2), and correlation coefficient (R). Overall evaluations suggested that the Deep ESN5 model provided the most reliable predictions of BOD among all the models at both stations.

In this study, the potential of six different machine learning models, gradient boosting tree (GBT), multilayer perceptron neural network (MLPNN), two types of adaptive neuro-fuzzy inference systems (ANFIS) based on fuzzy c-means clustering (ANFIS-FCM) and subtractive clustering (ANFIS-SC), multivariate adaptive regression spline (MARS), and classification and regression tree (CART) were used for forecasting solar radiation from two stations of two different locations, Turkey and USA. Wind speed, maximum air temperature, minimum air temperature and relative humidity were used as inputs to the developed models. For accurate evaluation of performance of models, four statistical indicators, root mean squared error (RMSE), coefficient of correlation (R), mean absolute error (MAE) and Nash–Sutcliffe efficiency coefficient (NS) were employed to evaluate accuracy of the developed models. Comparison of results showed that the GBT model performed better than the MLPNN, ANFIS, MARS, and CART in modeling solar radiation. The average RMSE of MLPNN, ANFIS-FCM, ANFIS-SC, MARS and CART models was decreased by 0.26%, 1.5%, 0.51%, 2.5%, and 19.34% using GBT model at Fairfield Station, 4%, 1.37%, 0.24%, 4.12%, and 24.4% at Monmouth Station, 11.99%, 48.7%, 41.6%, 8.23%, and 33.41% at Antalya Station, 11%, 54.8%, 51.9%, 19.65%, and 37.1% at Mersin Station, respectively. The overall results indicated that the GBT model could be successfully applied in forecasting solar radiation by using climatic parameters as inputs.

Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs (ELM, LSSVR, and ANN). As a result, the VMD-based MLMs provide better accuracy compared with the single MLMs and yield slightly better performance than the DWT-based MLMs. Among all models, the VMD-ELM and VMD-LSSVR models achieve the best performance in daily rainfall-runoff modeling with respect to efficiency and effectiveness. Therefore, the VMD-ELM and VMD-LSSVR models can be an alternative tool for reliable and accurate daily rainfall-runoff modeling.

This paper proposes hybrid soft computing models for daily river stage modeling. The models combine variational mode decomposition (VMD) with different soft computing models, including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF). The performances of VMD-based models (VMD-ANN, VMD-ANFIS, and VMD-RF) are assessed by model efficiency indices and graphical comparison, and compared with those of single models (ANN, ANFIS, and RF) and ensemble empirical mode decomposition (EEMD)-based models (EEMD-ANN, EEMD-ANFIS, and EEMD-RF). Results show that VMD-ANN, VMD-ANFIS, and VMD-RF models are more efficient and accurate than ANN, ANFIS, and RF models, respectively, and slightly better than EEMD-ANN, EEMD-ANFIS, and EEMD-RF models, respectively. In terms of model efficiency and accuracy, the top five models are VMD-ANFIS, EEMD-ANFIS, VMD-ANN, VMD-RF, and ANFIS and the VMD-ANFIS model is the best. It is found that VMD can enhance the performance of conventional single soft computing models; VMD is more effective than EEMD for hybrid model development; and the ANFIS model combined with VMD and EEMD can yield better efficiency and accuracy than other models. Therefore, VMD-based hybrid modeling is a more effective method for reliable daily river stage modeling.

We report on Snapdragon™ SDM845 mobile SoC in mass production with a second-generation 10-nm finFET technology. SDM845 exhibits 30–40% CPU/GPU performance gain over SDM835 (first-generation 10-nm finFET process) together with ~10% battery life increase driven by new design features and technology improvements in both transistor performance and uniformity, enabling high performance and low power solution for both mobile and computing/AI applications. Extending the technology scaling further, ~15% logic circuit area scaling over 10 nm has been realized in an 8-nm node with gate and BEOL pitch scaling enabled by quadruple patterning (LE^4). Yield equivalence to 10 nm has been demonstrated in 8-nm IP chips.

Recently, the availability of protocols supporting genetic complementation of Eimeria has raised the prospect of generating transgenic parasite lines which can function as vaccine vectors, expressing and delivering heterologous proteins. Complementation with sequences encoding immunoprotective antigens from other Eimeria spp. offers an opportunity to reduce the complexity of species/strains in anticoccidial vaccines. Herein, we characterise and evaluate EtAMA1 and EtAMA2, two members of the apical membrane antigen family of parasite surface proteins from Eimeria tenella. Both proteins are stage-regulated, and the sporozoite-specific EtAMA1 is effective at inducing partial protection against homologous challenge with E. tenella when used as a recombinant protein vaccine, whereas the merozoite-specific EtAMA2 is not. In order to test the ability of transgenic parasites to confer heterologous protection, E. tenella parasites were complemented with EmAMA1, the sporozoite-specific orthologue of EtAMA1 from E. maxima, coupled with different delivery signals to modify its trafficking and improve antigen exposure to the host immune system. Vaccination of chickens using these transgenic parasites conferred partial protection against E. maxima challenge, with levels of efficacy comparable to those obtained using recombinant protein or DNA vaccines. In the present work we provide evidence for the first known time of the ability of transgenic Eimeria to induce cross protection against different Eimeria spp. Genetically complemented Eimeria provide a powerful tool to streamline the complex multi-valent anticoccidial vaccine formulations that are currently available in the market by generating parasite lines expressing vaccine targets from multiple eimerian species.

Toll-like receptor (TLR) ligands (TLR-Ls) are critical activators of immunity and are successfully being developed as vaccine adjuvants in both mammals and birds. In this study, we investigated the synergistic effect of co-stimulation of membrane and endosomal TLRs on the innate immune responses using chicken bone marrow-derived macrophages (BMMs), and studied the effect of age on the induction of innate responses. BMMs from 1 and 4-week-old birds were stimulated with Pam3Cys-SK4 (PCSK; TLR2), synthetic monophosphoryl lipid A(MPLA), Di[3-deoxy-D-manno-octulosonyl]-lipid A ammonium salt (KLA; TLR4),Gardiquimod, Resiquimod (R848; TLR7), CpG class B and C (TLR21). Nitric oxide (NO) production and mRNA levels of IL-1, IL-10 and IL-12p40 showed macrophages from 4-week-old birds showed more sensitive responses compared to 1-week-old birds. The most potent TLR-Ls, PCSK, MPLA and CpG B were used to study the effect of co-stimulation on macrophages. Co-stimulation with TLR21 and TLR4 synergistically up-regulated inflammatory-related genes, as well as NO production. However, incubation of splenocytes with PCSK, MPLA and CpG B did not induce cell proliferation. Moreover, treatment with CpGB led to significant cell death.

Additional file 3. Analysis of TLR4 transcription in chBMMs stimulated wild-type C. jejuni and capsule or MeOPN mutants. Live or fixed C. jejuni 11168H (WT), Δcj1417 (MeOPN) and Δcj1439 (capsule) mutants were incubated with chBMMs at MOI 50 for 3 h. Salmonella LPS (100 ng/mL) was used as a positive control and PBS in the medium as a negative control. Graphs show representative results of three independent experiments, and error bars are the standard deviation of three independent experiments. Asterisks indicate a statistically significant difference of TLR4 transcripts compared to PBS in the medium (P

This paper investigates the accuracy of three different adaptive neuro-fuzzy inference systems (ANFISs), ANFIS with grid partition (ANFIS-GP), ANFIS with substructive clustering (ANFIS-SC),...

Accurate prediction of daily pan evaporation (PE) is important for monitoring, surveying, and management of water resources as well as reservoir management and evaluation of drinking water supply systems. This study develops and applies soft computing models to predict daily PE in a dry climate region of south-western Iran. Three soft computing models, namely the multilayer perceptron-neural networks model (MLP-NNM), Kohonen self-organizing feature maps-neural networks model (KSOFM-NNM), and gene expression programming (GEP), were considered. Daily PE was predicted at two stations using temperature-based, radiation-based, and sunshine duration-based input combinations. The results obtained by the temperature-based 3 (TEM3) model produced the best results for both stations. The Mann-Whitney U test was employed to compute the rank of different input combination for hypothesis testing. Comparison between the soft computing models and multiple linear regression model (MLRM) demonstrated the superiorit...

A hybrid model, combining regression kriging and neural network residual kriging (RKNNRK), is developed for determining spatial precipitation distribution. The RKNNRK model is compared with current spatial interpolation models, including simple kriging (SK), ordinary kriging (OK), universal kriging (UK), regression kriging (RK) and neural network residual kriging (NNRK). Results show that hybrid models, including RK, NNRK and RKNNRK, performed better than SK, OK and UK, based on the coefficient of efficiency (CE), coefficient of determination (r 2), index of agreement (d), mean squared relative error (MSRE), mean absolute error (MAE), root-mean-square error (RMSE), and mean squared error (MSE). Of the six spatial interpolation models, the RKNNRK model was the most accurate, and the NNRK model was the second best.

Accurate forecasting of floods is vital for developing a flood warning systems, flood prevention, flood damage mitigation, soil erosion reduction and soil conservation. The objective of this study is to apply two hybrid models for flood forecasting and investigate their accuracy for different lead times. These two models are the Wavelet-based Artificial Neural Network (WANN) and the Wavelet-based Adaptive Neuro-Fuzzy Inference System (WANFIS). Wavelet decomposition is employed to decompose the flood time series into approximation and detail components. These decomposed time series are then used as inputs of Artificial Neural Network (ANN) and adaptive Neuro-Fuzzy Inference System (ANFIS) modules in the WANN and WANFIS models, respectively. The WANN and WANFIS models yielded better results than the ANN and ANFIS models for different lead times. The WANN and WANFIS models performed almost similarly. However, in terms of model efficiency, the WANFIS model was superior to other models for lead times of 1 to 6 hours, and the WANN model was superior to other models for lead time of 8 to 10 hours. The results obtained from this study indicate that the combination of wavelet decomposition and data-driven models, including ANN and ANFIS, can improve the efficiency of data-driven models. Results also indicate that the combination of wavelet decomposition and data-driven models can be a potential tool for forecasting flood stage more accurately.

Long-term selection for juvenile body weight from a common founder population resulted in two divergent chicken lines (low-weight selected line (LWS), high-weight selected line (HWS)) that display distinct food intake and blood glucose responses to exogenous neuropeptides and insulin. The objective of this study was to elucidate putative targets affecting food intake and energy homeostasis by sequencing hypothalamic RNA from LWS and HWS chickens after insulin injection. Ninety-day-old female LWS and HWS chickens were injected with either vehicle or insulin and hypothalamus collected at 1 h postinjection. Through RNA sequencing, a total of 361 differentially expressed genes (DEGs) were identified. There was greater expression of genes, mainly tyrosine hydroxylase (TH), L-aromatic amino acid decarboxylase (DDC), and vesicular monoamine transporter (VMAT), involved in serotonin and dopamine biosynthesis and signaling in LWS than in HWS vehicle-injected chickens. In contrast, after insulin injection, these genes were more highly expressed in HWS than in LWS. We identified 90 single nucleotide polymorphisms (SNPs) existing only in the HWS and 121 SNPs specific to LWS and 5119 SNPs close to fixation (with absolute frequency difference ≥0.9). Four were located in genes encoding enzymes associated with serotonergic and dopaminergic pathways, such as DDC, TH, and solute carrier family 18, member 2 (VMAT). These data implicate differences in biogenic amines such as serotonin and dopamine in hypothalamic physiology between the chicken lines, and these differences might be associated with polymorphisms during long-term selection. Changes in serotonergic and dopaminergic signaling pathways in response to insulin injection suggest a role in whole-body energy homeostasis.

Summary Reliable water level forecasting for reservoir inflow is essential for reservoir operation. The objective of this paper is to develop and apply two hybrid models for daily water level forecasting and investigate their accuracy. These two hybrid models are wavelet-based artificial neural network (WANN) and wavelet-based adaptive neuro-fuzzy inference system (WANFIS). Wavelet decomposition is employed to decompose an input time series into approximation and detail components. The decomposed time series are used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WANN and WANFIS models, respectively. Based on statistical performance indexes, the WANN and WANFIS models are found to produce better efficiency than the ANN and ANFIS models. WANFIS7-sym10 yields the best performance among all other models. It is found that wavelet decomposition improves the accuracy of ANN and ANFIS. This study evaluates the accuracy of the WANN and WANFIS models for different mother wavelets, including Daubechies, Symmlet and Coiflet wavelets. It is found that the model performance is dependent on input sets and mother wavelets, and the wavelet decomposition using mother wavelet, db10, can further improve the efficiency of ANN and ANFIS models. Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can be a useful tool for accurate forecasting daily water level and can yield better efficiency than the conventional forecasting models.